State estimation is a central application for the management of a power system network. Two types of state estimators, namely, static and dynamic, are possible for realization. Traditionally, static state estimation techniques are used by the electric power industry to estimate the state of power transmission and distribution systems, due to the relative simplicity of the techniques and the ready availability of supervisory control and data acquisition (SCADA) data, which is often obtained at relatively slow sampling rates. Dynamic state estimation, on the other hand, would allow power system operators to observe and respond to transient state changes in the power system, and is likely to become more relevant with the increasing availability of fast-sampled sensor data, such as phasor measurement unit (PMU) data.
Some approaches to dynamic state estimation of large-scale power systems are based on the use of model-based filtering techniques, which include techniques based on dynamic observers, such as linear parameter-varying (LPV) dynamic observers or proportional-integral (PI) observers. Observer-based power system monitors can be used to estimate the complete state of a power transmission and distribution system, as well as to identify and isolate a number of events (e.g., faults), using only sparse local measurements, all in the presence of various system disturbances. One such approach, based on the graphical design of LPV dynamic observers, is described in detail by E. Scholtz and B. C. Lesieutre, “Graphical Observer Design Suitable for Large-scale DAE Power Systems,” Proceedings of the 47th IEEE Conference on Decision and Control, Cancun Mexico, Dec. 9-11, 2008, pp. 2955-60 (hereinafter referred to as “the Scholtz and Lesieutre article”), the complete disclosure of which is incorporated by reference for all purposes.
Dynamic state estimation promises significant advantages compared to the static estimation approach, including higher accuracy and an ability to capture the dynamics of the network. On the other hand, dynamic state estimation for monitoring and control purposes requires real-time (or faster than real-time) simulation of the system models, which are generally more complex than the static state models and thus involve greater computational complexity. While the computational requirements for dynamic state estimation can be manageable for relatively small power transmission and distribution networks, these increased computational requirements can make it prohibitively expensive or even impossible to compute the solution in a timely manner using conventional techniques, for large networks.